Luxembourg Wealth Study

Working Paper Series

Luxembourg Income Study (LIS), asbl

Working Paper No. 1

Comparing wealth distribution accross rich countries: First results from the Luxembourg Wealth Study Eva Sierminska, Andrea Brandolini and Timothy M. Smeeding

COMPARING WEALTH DISTRIBUTION ACROSS RICH COUNTRIES: FIRST RESULTS FROM THE

LUXEMBOURG WEALTH STUDY1

Eva Sierminska (Luxembourg Income Study),

Andrea Brandolini

(Banca d’Italia, Economic Research Department)

and

Timothy M. Smeeding (Syracuse University and Luxembourg Income Study)

August 9th, 2006

Abstract The new Luxembourg Wealth Study (LWS) creates for the first time a harmonized cross national database on household assets and liabilities. This paper describes the project, outlines conceptual and practical issues that need to be addressed in preparing harmonized and comparable wealth data across countries, and summarizes the initial results for the eight countries participating in the initial phase: Canada, Cyprus, Finland, Germany, Italy, Norway, Sweden, the United Kingdom and the United States.

Contents 1. Introduction.......................................................................................................2 2. Genesis, goals and participants.........................................................................4 3. A sketch of data sources ...................................................................................6 4. LWS variables and wealth classification..........................................................8 5. Further comparability issues...........................................................................10 6. First results from the LWS database...............................................................13

6.1. Asset and debt participation and portfolio composition ..........................13 6.2. The distribution of net worth: means, medians and inequality................15

7. Conclusions.....................................................................................................17 References..............................................................................................................20

1 Eva Sierminska (sierminska@lisproject.org) is the LWS project co-ordinator, Andrea Brandolini (andrea.brandolini@bancaditalia.it) and Timothy Smeeding (tmsmeed@maxwell.syr.edu) the LWS project leaders. Further information on the LWS project is available at http://www.lisproject.org/lws.htm. We are very grateful to all sponsoring institutions and participants in the LWS project. We thank Markus Säylä and Ulf von Kalckreuth for providing us with unpublished data for Finland and Germany, respectively. The views expressed here, however, are solely ours, and do not necessarily reflect those of any of the sponsoring institutions.

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1. Introduction

The study of the distribution and composition of household wealth is a flourishing

research field. Empirical analysis must, however, cope with considerable weaknesses in the

available data. Household surveys of assets and debts, for instance, typically suffer from large

sampling errors due to the high skewness of the wealth distribution as well as from serious

non-sampling errors. In comparative analysis these problems are compounded by differences

in the methods and definitions used in various countries. Indeed, in introducing a collection of

essays on household portfolios in five countries, Guiso, Haliassos and Jappelli mention

“definitions” as the “initial problem” and warn the reader that “the special features and

problems of each survey … should be kept in mind when trying to compare data across

countries” (2002, pp. 6-7). Likewise, Davies and Shorrocks conclude their extensive survey

on the distribution of wealth by remarking that: “Adoption of a common framework in

different countries, along the lines that have been developed for income distributions, would

improve the scope for comparative studies” (2000, p. 666).

The contrast with income is an apt one. By now, also thanks to the endeavor of the

Luxembourg Income Study (LIS), we have a good idea of the income inequality ranking of

OECD countries (e.g., Brandolini and Smeeding, 2005; 2006). At the turn of the century,

income inequality was least in Nordic countries. The Benelux countries, France, Germany and

other Central and Eastern European countries came next, preceding most Anglo-Saxon

nations and the Southern European countries. The United States, Estonia, Mexico and Russia

exhibited the highest degree of inequality. While we can draw this income inequality picture

with some confidence, our knowledge is far more uncertain on the country ordering in terms

of wealth inequality. A recent compilation of data for nine nations around the beginning of

this decade shows that Sweden, not the United States, leads the ranking (Brandolini, 2006,

figure 2, p. 48). This evidence not only runs counter to that based on income, but also to

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earlier evidence. According to the figures assembled by Davies and Shorrocks (2000, table 1,

p. 637) for 11 nations, in the mid 1980s wealth inequality was among the lowest in Sweden

and greatest in the United States. Does this different ranking reflect true changes during the

1990s, or are we reacting to some statistical artifact? If one leans toward the latter

explanation, we might turn to the results obtained by Klevmarken, Lupton and Stafford

(2003), showing the much higher inequality of the U.S. wealth distribution in the 1980s and

1990s.2 This is a clear warning that before making cross-country comparisons and

investigating the causes of different patters, we must carefully understand the extent to which

data are comparable.

These and similar questions have led researchers and institutions from a number of

countries to join forces to launch the Luxembourg Wealth Study (LWS) – an international

project to assemble existing micro-data on household wealth into a coherent database. As the

LIS experience has clearly shown, the availability of such database is likely to spur

comparative research on household net worth, portfolio composition, and wealth distributions,

and to stimulate a process of harmonization of definitions and methodologies. The purpose of

this paper is to sketch the main features of the project and to present the first preliminary

results, in order to show the potential of the LWS database. While we take full responsibility

of what is written here, it is important to recognize from the outset that we owe a great debt to

all sponsoring institutions and participants in the LWS project. In a sense, this paper is a

collective effort much more than is revealed by the names of the authors of this paper alone.

2 Klevmarken (2006, pp. 30-1) reports that, in 2003, the inequality of net worth was in Sweden somewhat below the average, and lower than in France, Germany and Italy, according to the evidence of the Survey of Health Ageing and Retirement in Europe (SHARE) – an international project for the collection of data standardised from the outset on the living conditions and health status of the households with at least one member aged 50 and more.

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The paper is organized as follows. The next Section describes the genesis and structure

of the project. Sections 3 to 5 summarize the main features of data sources and discuss the

classification of wealth variables and some comparability issues. Preliminary results from the

β-version (test version) of the LWS database are discussed in Section 6. Section 7 concludes.

2. Genesis, goals and participants

The idea of the Luxembourg Wealth Study originated at the 27th General Conference

of the International Association for Research in Income and Wealth, held in Djurhamn,

Sweden in August 2002. Following the discussion in a session on the size distribution of

wealth, it was apparent that data on household net worth were far behind those on income in

terms of international comparability. It was then recognized that the time was ripe for the

creation of a cross-country comparable wealth database. The LIS successful experience,

begun almost two decades earlier (Smeeding, 2004), suggested the way forward: a

cooperative project gathering producers of wealth micro-data in countries where these data

were available. After two more meetings of wealth and data collection experts in 2003, one at

LIS offices in Luxembourg in July and one at the Levy Economics Institute in New York in

October, the LWS was officially launched in March 2004 as a joint project of LIS and

institutions from nine countries: Canada, Cyprus, Finland, Germany, Italy, Norway, Sweden,

the United Kingdom, and the United States. Austria has also joined in spring 2006, making

LWS a ten nation enterprise at present.

The primary goal of the project is to assemble and to organize existing micro-data on

household wealth into a coherent database, in order to provide a much more sound basis for

comparative research on household net worth, portfolio composition, and wealth distributions.

The ex post harmonization of existing data is seen as the first stage of the project. The

establishment of a network of producers and experts of data on household net worth aims at

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promoting a process of ex ante standardization of definitions and methodologies. The

elaboration of guidelines for the collection of household wealth statistics, as done for income

by the Expert Group on Household Income Statistics–The Canberra Group (2001), is an

important task for the foreseeable future. In light of these goals the first workshop on the

“Construction and Usage of Comparable Microdata on Wealth: the Luxembourg Wealth

Study” was organized by Banca d’Italia in Perugia, Italy in January, 2005. The outcome of

this conference was a series of technical papers available on the LWS website, which provide

the basis for future discussions in constructing comparable wealth survey data.

Participants in the LWS project are a varied group. Sponsoring institutions include

statistical offices (Statistics Canada, Statistics Norway), central banks (Central Bank of

Cyprus, Banca d’Italia, Österreichische Nationalbank), research institutes (Deutsches Institut

für Wirtschaftsforschung–DIW, U.K. Institute for Social and Economic Research–ISER,

through a grant awarded by the Nuffield Foundation), universities (Åbo Akademi University),

and research foundations (Finnish Yrjö Jahnsson Foundation, Palkansaajasäätiö –Finnish

Labour Foundation, Swedish Council for Working Life and Social Research–FAS, U.S.

National Science Foundation). Representatives from several other public institutions

(Statistics Sweden, Banco de España, De Nederlandsche Bank, U.S. Federal Reserve Board,

U.S. Internal Revenue Service, U.K. Department for Work and Pensions, Organisation for

Economic Co-operation and Development, World Bank) as well as researchers from many

universities have taken part in different stages of the project.

The partnership with the LIS is a strong asset, as it allows the LWS project to take

advantage of the 20-year LIS experience in harmonizing household survey data and making

them accessible to researchers world-wide through an innovative remote access system (see

http://www.lisproject.org for further details). The same access rules will be followed by the

LWS. The β-version (test version) of the database has been released and is being tested by

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researchers participating in the project. The comparison of the β-version of the database with

the original national sources will be the object of a technical workshop planned for December

2006. The test phase will lead to the preparation of the final α-version of the database that is

expected to be made public sometime in 2007. The release of the α-version to the research

community will mark the end of the first stage of the LWS project. Afterwards, the

maintenance and updating of the dataset will be part of the regular LIS activities, as decided

by the board of LIS country members in July 2005 and to be discussed again in July 2007. As

for LIS, participation in the LWS work will be open to any country that has the relevant

information and wants to join the project.3

3. A sketch of data sources

The data sources included in the LWS database and some of their characteristics are

listed in Table 1. (The Austrian survey is covered here for sake of completeness but no further

comments will be made in the paper, as the work to include this survey in the LWS database

has just started.) Although all countries rely on sample surveys among households or

individuals, there are differences in collection methods across surveys. For example, in two

Nordic countries the data are supplemented with information from administrative records

(mostly wealth tax registers). Some income information is also supplemented by tax registers

in Canada and Finland. Sample sizes are widely different, ranging from 895 households in

Cyprus to 22,870 units in Norway.

The surveys also differ by purpose and sampling frame (see Sierminska, 2005, for

further details). Certain surveys have been designed for the specific purpose of collecting

wealth data (CA-SFS, CY-SCF, IT-SHIW, US-SCF), whereas others cover different areas and

3 Participation in the LWS project has already been discussed with the Netherlands, New Zealand, Spain, and other similar nations.

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have been supplemented with special wealth modules of longitudinal household panel surveys

(GE-SOEP, UK-BHPS, US-PSID). Some surveys over-sample the wealthy and provide a

better coverage of the upper tail of the distribution (CA-SFS, CY-SCF, GE-SOEP, US-SCF),

but at the cost of higher non-response rates. Others ask only a small number of broad wealth

questions, but achieve good response rates (e.g., US-PSID). Germany applies a special case of

“bottom-coding,” because financial assets, durables and collectibles, and non-housing debt

are only recorded when their respective values exceed 2,500 euros. Tax registers may contain

more precise estimates, but they suffer from underreporting due to tax evasion and tax

exemptions, or to valuation criteria based on fiscal or administrative rules rather than market

prices (see below).

Definitions are also not uniform across surveys. In general, the unit of analysis is the

household, but it is the individual in Germany, and the nuclear family (i.e. a single adult or a

couple plus dependent children) in Canada. A household is defined as including all persons

living together in the same dwelling, but sharing expenses is an additional requirement in

Cyprus, Italy, Finland, Norway, Sweden and the United States. This implies that demographic

differences reflect both the definition of the unit of analysis and true differences in the

population structure.

The household’s head is defined as the main income earner in most surveys, but it is

defined as the person most knowledgeable and responsible for household finances in

Germany, Italy and the United Kingdom. The United States is the only country where the

head is taken to be the male in a mixed-sex couple. Multiple household’s heads are allowed in

Norway wherever the partners in a couple are not married or cohabiting, or adult children are

present, since the head is defined with reference to each nuclear family within the household.

As in the LWS database the unit is taken to be the household, in these cases the household’s

head has been identified with the main income earner.

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The surveys included in the LWS archive differ in many other respects, and some

more closely related to wealth variables are discussed in the next Section. Full documentation

of each survey’s features will be an important constituent of the LWS archive. The LWS

documentation will also report which of these differences in the original surveys were

corrected for in the harmonization process, and which were not.

4. LWS variables and wealth classification

The number and definition of recorded wealth variables vary considerably across

surveys. As shown in Table 1, the number of wealth categories ranges from a minimum of 7

in the UK-BHPS to 30 or more in the IT-SHIW, the NO-IDS and the US-SCF. This number

compounds with the detail of the questions: in some surveys, there are few simple summary

questions; in other surveys, the very high level of detail leads to a considerable multiplication

of the number of separate recorded items. The US-SCF is by far the most detailed wealth

survey of those included in the LWS database: checking accounts, for instance, are first

separated into primary and secondary accounts, and then distinguished according to the type

of bank where they are held.

The great variation in the amount of recorded information makes the construction of

comparable wealth aggregates a daunting task. This problem has been approached by defining

an ideal set of variables to be included in the LWS database. This starts with a general

classification of wealth components, from which totals and subtotals are obtained by

aggregation. This set is then integrated with demographic characteristics (including health

status) and income and consumption aggregates, plus a group of variables particularly

relevant in the study of household wealth: realized lump-sum incomes (e.g., capital gains,

inheritances and inter-vivo transfers) and “behavioral” variables such as motives for savings,

perceptions about future events (e.g., bequest motivation), attitude towards risk, and so forth.

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This ideal list has been pared down after a comparison with the information actually

available in the LWS surveys. With regards to wealth, this process has eventually led to

identify the following categories:

• Financial assets: Transaction and savings accounts, CDs; Total bonds; Stocks; Mutual

and investment funds; Life insurance; Pension assets; Other financial assets.

• Non-financial assets: Principal residence, Investment real estate; Business equity;

Vehicles; Durables and collectibles; Other non-financial assets.

• Liabilities: Home secured debt, which is the sum of Principal residence mortgage, Other

property mortgage, and Other home secured debt (including lines of credit); Vehicle

loans; Installment debt (including credit card balance); Educational loans; Other loans

from financial institutions; Informal debt.

• Net worth: Financial assets plus Non-financial assets less Liabilities.

Crossing this classificatory grid with the information available in each LWS survey

gives rise to the matrix of Table 2. This Table illustrates the difficulty of transforming the

original sources into a harmonized database: coverage and aggregation of wealth items vary

widely across surveys. An acceptable degree of comparability can be obtained for four main

categories of financial assets: deposit accounts, bonds, stocks, and mutual funds – with the

partial exception of Germany which does not record information on checking deposits. The

remaining financial components are available only for some countries. For non-financial

assets the greatest comparability is obtained for principal residence and investment real estate.

Liabilities are present in all surveys, though with a varying degree of detail. Applying the

minimum common denominator criterion to this matrix, the following four LWS aggregates

are defined: total financial assets, including deposit accounts, stocks, bonds, and mutual

funds; non-financial assets, including principal residence and investment real estate; total

debt; and net worth, i.e. the sum of financial and non-financial assets net of total debt.

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Business equity is not available for all nations, but is comparable for at least seven nations. If

one is willing to focus on a smaller subset of nations, more complete definitions are possible.

These LWS aggregates, on which we focus in the next Sections, are broadly

comparable, but fall far short of perfect comparability, since underlying definitions and

methods vary across surveys. Moreover, these aggregates fail to capture important wealth

components, such as business equity and pension assets. As their importance differs across

countries, cross-national comparisons are bound to reflect these omissions. Some indication is

provided by the comparison between the LWS definitions and the national definitions of net

worth. The LWS database includes the variables which are part of the national concept but are

excluded from the LWS definition. This allows users to reconcile the different definitions, as

shown in Table 3 for five countries. The first message of Table 3 is reassuring: once the

missing items are included back in net worth, the LWS figures closely approximate those

released in official publications. On the other hand, more worryingly, the weight of these

omissions is significant and varies considerably across countries: it goes from about a half in

the two North-American nations to less than a fourth in the three European nations of Table 3.

This evidence is a salutary warning of the currently high cost of cross-country comparability

using current survey practices: until a greater standardization of wealth surveys is achieved ex

ante, we have to trade off higher comparability against a somewhat incomplete picture of

national wealth.

5. Further comparability issues

Other methodological differences, in addition to those concerning definitions, affect

comparability. Some relate to the way assets and liabilities are recorded (as point values, by

brackets, or both) and to their accounting period. Wealth values generally refer to the time of

the interview, but in four countries end-of-year values are registered (Table 1). Moreover, in

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half of the surveys included in the LWS database the reference period for income differs from

that for wealth.

The criteria to value assets and liabilities may differ too (see Atkinson and Harrison,

1978, pp. 5-6). In most cases, wealth components are valued on a “realization” basis, or “the

value obtained in a sale on the open market at the date in question” (Atkinson and Harrison,

1978, p. 5), as estimated by the respondent. But there are important exceptions, the most

relevant being the valuation of real property in Sweden and Norway on a taxable basis.

Statistics Sweden calculates the ratios of purchase price to tax value for several types of real

estate and geographical locations, and then use them to inflate the tax values registered in the

survey. This procedure is however not applied to Norwegian data, although Statistics Norway

estimated that in the 1990s the taxable value of houses was less than a third of their market

value (see Harding, Solheim and Benedictow, 2004, pp. 15-6, fn. 10). These diverse choices

are likely to affect comparisons between the two Scandinavian countries as well as between

them and the other countries relying on valuation at market prices as estimated by

respondents.

Lastly, there are different patterns of non-response and different imputation

procedures. For instance, the CY-SCF has a rather detailed set of questions, but the number of

missing values is very high: only 349 households, out of 895, provided enough information to

estimate the LWS net worth concept (Table 4). The overall response rate of the IT-SHIW is

rather low, about 36 per cent in the 2002 wave, net of units not found at the available address,

but item non-responses are few. LWS net worth cannot be derived for 14 per cent of the

households in the UK-BHPS. Banks, Smith and Wakefield (2002) have applied a “conditional

hot-deck” imputation method at the benefit unit level to alleviate the missing information

problem, but it is still to be determined whether LWS will follow the same methodology. In

the US-PSID financial assets as well as housing equity are imputed. Discussions are under

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way whether this imputation method can be followed to obtain values for the principal

residence and mortgages that would reduce the overall proportion of missing values. In the

US-SCF item non response is tackled by using a sophisticated multiple imputation program

(Kennickell, 2000), while in the GE-SOEP it is currently treated by simply replacing missing

values with the overall mean (a complex imputation procedure is under study).

A synthetic assessment of the information contained in the LWS database is provided

by the comparison of LWS-based estimates with their aggregate counterparts in the national

balance sheets of the household sector (which include non-profit institutions serving

households and small unincorporated enterprises). This comparison is presented in Table 5,

where all variables are transformed into euro at current prices by using the average market

exchange rate in the relevant year, and are expressed in per capita terms to adjust for the

different household size. The aggregate accounts provide a natural benchmark to assess the

quality of the LWS database, but a proper comparison would require a painstaking work of

reconciliation of the two sources, as discussed at length by Antoniewicz et al. (2005). The aim

of Table 5 is more modestly to offer a summary view of how the picture drawn on the basis of

the LWS data relate to the one that could be derived from the national balance sheets or the

financial accounts. LWS estimates seem to represent non-financial assets and, to a lesser

extent, liabilities better than financial assets. In all countries where the aggregate information

is available, the LWS wealth data account for between 40 and 60 per cent of the aggregate

wealth. Note that these discrepancies should not be attributed to the deficiency of the LWS

data, since they reflect not only the under-reporting in the original micro sources, but also the

dropping of some items in the LWS definitions to enhance cross-country comparability as

well as the different definitions of micro and macro sources.

To sum up, despite the considerable effort put into standardizing wealth variables,

there remain important differences in definitions, valuation criteria and survey quality that

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cannot be adjusted for at this time. Moreover, the degree to which LWS-based estimates

match aggregate figures varies across surveys. These observations have to be borne in mind in

reading the results discussed in the next Section.

6. First results from the LWS database

In this Section we present some descriptive evidence on household wealth for the nine

countries included in the β-version of the LWS database. We focus on asset and debt

participation, portfolio composition, and the distribution of net worth. As wealth

accumulation patterns vary over the life-cycle, it is useful to portray the demographic

structure in each country before reviewing this evidence (Table 6). The average household

size ranges from 1.96 persons in Sweden to 2.65 in Italy and 3.35 in Cyprus. Italy stands out

as the country with the most pronounced ageing process. On average, the age of household’s

heads is 55 years in Italy, against 53 in the United Kingdom, 52 in Germany and 51 in

Sweden; in all other countries, mean age is below 50, with a minimum 47 in Canada. Italy has

both the lowest share of young household’s heads (below 35 years) and the highest share of

old heads (more than 64 years): 10 and 33 per cent, respectively. The United Kingdom and

Germany follow at some distance. At the other extreme, 18 per cent of the Canadian

households are headed by an old person, and 27 per cent of households in Norway are headed

by a young one. In other countries, old household’s head account for around 21-22 per cent of

the total and young heads for about 23-24 per cent.

6.1. Asset and debt participation and portfolio composition

Table 7 shows that in almost all LWS countries, over 80 per cent of households own

some financial asset. In most countries this is a deposit account. Stocks are particularly spread

in Cyprus, Finland and Sweden. Sweden and Norway have the highest diffusion of mutual

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funds. 44 per cent of Cypriot households hold bonds. In the United States, according to the

SCF, holders of stocks, bonds and mutual funds each account for about a fifth of the

population. Over 60 per cent of households own their principal residence in all countries

except in Germany and Sweden: the proportion is highest in Cyprus (74 per cent), and it falls

just below 70 per cent in Italy, the United Kingdom and the United States (SCF). Owning a

second home is most popular in Finland and Norway. There is substantial variation in debt

holdings: from 22 per cent of households in Italy to 80 in Norway; from 10 per cent in Italy to

46 in the United States if only home secured debt is considered.

As noticed above, most of financial assets and non-housing debt are recorded in

Germany only if they exceed 2,500 euros. The figures in the bottom panel of Table 7 are

obtained by applying the same bottom coding to the data for six other countries, in order to

put them on a comparable basis with the German data (something which LWS flexibility

allows the user to accomplish). The share of households owning financial assets is now in

Canada and Finland similar to the German one; it is 20 percentage points higher in Italy and

Norway, with the two Angolo-Saxon countries in an intermediate position. The comparison

between the top and bottom panel of the Table indicates that a large proportion of Canadian

and Finnish households holds very little in reported financial assets.

One of the advancements allowed by the availability of the LWS database is, however,

in the demonstration of different patterns of comparative wealth holding among households.

The age profiles for the possession of financial assets, principal residence, debt and positive

net worth are significantly different across countries (Figure 1). Italy, again, stands out as an

outlier. On the one hand, intergenerational differences appear to be dissimilar, since the

hump-shape of debt-holding and home-ownership is much flatter than in the other countries.

On the other hand, the low propensity to borrow and the parallel high proportion of positive

net worth holders, already noted for the average, are common across all age classes. Norway

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and Finland show a remarkable diffusion of financial wealth in all cohorts, including the

young. In Germany and Sweden the share of home-owners tends to be lower than in other

countries, and it is markedly so among the elderly.

Table 8 shows a considerable variance in portfolio composition.4 The United States

exhibits the highest preference for financial assets: around 35 per cent of total assets, over two

thirds of which are held in risky instruments like stocks and mutual funds. Sweden and

Canada follow, with proportions of 28 and 22 per cent, respectively. Financial instruments

account for only 15-16 per cent of total assets in Finland and Italy. The principal residence

represents 60 per cent or more of the value of total assets in all countries except the United

States, where it accounts for close to 50 per cent. The ratio of debt to total assets ranges from

a very low 4 per cent in Italy to 35 per cent in Sweden. Comparing the household portfolio

composition as measured in the LWS database with the composition emerging from aggregate

data is an important topic for future research.

6.2. The distribution of net worth: means, medians and inequality

Figure 2 indicates that country ranking differs between net worth and income, and also

that it matters which measure of central tendency of the wealth distribution is chosen: mean or

median. All values are expressed in international 2002 U.S. dollars by using the purchasing

power parities and consumer price indices estimated by the OECD. Both with the mean and

the median income, the United States is the richest country followed by Canada and the

United Kingdom, then Germany and Sweden, and lastly Finland and Italy. This is not the case

for net worth. The United States and Italy are the richest nations according to mean net worth,

4 Note that figures are not reported for Cyprus, owing to the many missing values, and for Norway because of the inconsistency stemming from valuing real estate on a taxable basis and debt at market prices. Also, the German data are biased by the fact that small holdings of some financial assets and debt are not recorded.

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and Sweden and Finland are at the poorest ones. Once we switch to the median, the United

States fall toward the middle and are surpassed by Finland and the United Kingdom. Italy and

the United Kingdom show by far the highest median net worth, almost twice the

corresponding values for the other countries.

Median wealth holdings by age of the household’s head in Figure 3 exhibit a similar

hump-shaped pattern, although at different levels of net worth, in most countries. The young

have less, the middle aged have the most, and the older have less than the middle-aged but

more than the young. The richest young are found in Cyprus and Italy, but their share in

population is small, suggesting that only those with enough wealth leave their parents’ house

(see also Martins and Villanueva, 2006, Table 1). In the United States, Canada, the United

Kingdom and Italy the older headed households are also quite well-off. The patterns for

financial assets are quite varied for those aged 50 and over. In all countries, the young have

little debt, while those aged 35-44 are the most indebted. Unsurprisingly, indebtedness is low

among the older age classes: indeed, over half of the elderly have no debt in all countries. In

Germany and Italy, over half of the households have no debt at all ages.

The LWS database allows us to shed new light on international differences in wealth

concentration. There are very few international comparisons of wealth distribution based on

micro-data reclassified to account for differences in definitions. Kessler and Wolff (1991),

Klevmarken, Lupton and Stafford (2003) and Faiella and Neri (2004) are among the few

examples of bilateral comparisons but, to our knowledge, the LWS project is the first attempt

to extend such comparisons to more than two countries. Table 9 shows statistics on the

distribution of net worth in seven countries. The caveats exposed in previous Sections must be

borne in mind: in particular, the bottom-coding implemented in the German survey is likely to

overstate measured inequality. According to the β-version of the LWS database, the highest

Gini index is found in Sweden. The United States closely follow, and Germany and Canada

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come next. Finland, the United Kingdom and Italy exhibit a more equal distribution of net

worth. Hence, also the LWS data put Sweden at the top of the ranking. In accounting terms,

part of the explanation rests on the very high proportion of Swedish households with nil or

negative net worth: 32 per cent against 23 per cent, at most, in the other countries (excluding

Germany, whose figure is probably overstated by bottom-coding). When the share of net

worth held by top population percentiles is considered, the United States regain the lead: the

richest one per cent of U.S. households controls 33 per cent of total wealth, according to the

SCF, or 25, according to the PSID, and the next four per cent controls another 25 per cent.5

These proportions are far higher than in all other countries, Sweden included. Understanding

the extent to which these results are affected by the different measurement methods or the

different comprehensiveness of the wealth definition is an important question left for future

LWS research. For instance, counting pension rights as an asset might matter more for

Sweden, resulting in much greater equality than found in the figures of Table 9.6

7. Conclusions

Reliable statistics on the composition and distribution of private wealth is a pre-

requisite for the study of the well-being of households and their consumption and financial

behavior. As stressed by John Campbell in his Presidential Address to the American Finance

Association early this year, “measurement” is a “challenge” faced by researchers studying

household finance:

“Positive household finance asks how households actually invest. This is a conceptually straightforward question, but it is hard to answer because the necessary data are hard to obtain. In the United States, households guard their financial privacy jealously: in fact, it may be more unusual today for people to reveal intimate details of

5 The over-sampling of the wealthy in the US-SCF but not in the US-PSID is a plausible reason for the difference in the estimated shares of the richest households. 6 On measuring pension wealth see Brugiavini, Maser and Sundén (2005).

18

their financial affairs than to reveal details of their intimate affairs. In addition, many households have complicated finances, with multiple accounts at different financial institutions, having different tax status, and including both mutual funds and individual stocks and bonds. Even households that wish to cooperate with researchers may have some difficulty answering detailed questions accurately.” (2006, p. 3, italics added).

The challenge of measurement is stretched to the limit when we move to comparative

analysis, since the difficulties in collecting data on household finances are compounded by the

need to standardize these data across countries. Yet, the exercise is very well worth taking.

In the first place, in a number of countries there are enough data which, once they are

properly treated, could shed light on cross-national differences in household finances. The

detailed work on the single items recorded in each of the surveys included in the LWS

database has allowed us to construct a set of variables and wealth aggregates which are

broadly comparable across countries. Researchers must be aware that many problems remain

and that comparative results must be taken with some caution. But the LWS project has

shown that cross-national analysis of household wealth holding is indeed feasible. This paper

has only given a flavor of the variance of wealth patterns across countries and raised further

research questions. Why is indebtedness so low among Italians and Germans at all ages? Why

are Italian and British households so much richer, on average, than Swedish and Finnish

households? Does this reflect differences in the asset valuation criteria, or a diverse balance

between public and private provision of life security? Is wealth inequality really higher in

Sweden than in the United States?

There is, however, a second important reason for the LWS endeavor. Comparing

micro and macro sources on household wealth across countries is an effective way lo learn

about relative weaknesses and methodological differences; it is instrumental in defining an

internationally agreed frame for the collection and classification of household wealth at the

individual level – as done in the past by LIS for income statistics. Cross-national differences

19

will never be eliminated entirely, and perfect comparability is hardly achievable. But the

LWS project provides a starting point for a much needed process of ex ante standardization of

methods and definitions. The release of the α-version of the LWS database to the scientific

community will allow a considerable progress in substantive research on household wealth on

a comparative basis, but it must also be seen as a first step toward the construction of better

cross-country comparable wealth data.

At this state, the LWS project is similar to where the LIS project was 20 years ago.

Definitions have been suggested, patterns have been identified and explanations are still to

emerge. But a sense of excitement is in the air. We know that LIS has paved the way to a

whole range of comparative cross-national studies by increasing the ratio of “signal” to

“noise” in comparative studies of income distribution, poverty, and inequality more generally

(Butz and Torrey, 2006). We can only hope that LWS can achieve similar status in

comparisons of wealth and net worth in decades to come.

20

References

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finances: Evidence from the 1998 and 2001 Survey of Consumer Finances”. Federal

Reserve Bulletin, January.

Antoniewicz, R., R. Bonci, A. Generale, G. Marchese, A. Neri, K. Maser and P. O’Hagan

(2005). “Household Wealth: Comparing Micro and Macro Data in Cyprus, Canada,

Italy and United States”, paper prepared for LWS Workshop: “Construction and Usage

of Comparable Microdata on Wealth: the LWS”, Banca d’Italia, Perugia, Italy, 27-29

January 2005.

Atkinson, A. B., and A. J. Harrison (1978). Distribution of Personal Wealth in Britain.

Cambridge: Cambridge University Press.

Banks, J., Z. Smith and M. Wakefield (2002). “The Distribution of Financial Wealth in the

UK: Evidence from 2000 BHPS Data”. Institute for Fiscal Studies, Working Paper No.

02/21, November.

Board of Governors of the Federal Reserve System (2006). Flow of Funds Accounts of the

United States. Flows and Outstandings. First Quarter 2006. Washington, DC. Available

at: http://www.federalreserve.gov/releases/z1/current/default.htm.

Brandolini, A. (2006). “The Distribution of Wealth in Germany and Sweden: Discussion of

the Papers by Stein and Klevmarken”. In G. Chaloupek and T. Zotter (eds.), Steigende

wirtschaftliche Ungleichheit bei steigendem Reichtum?, pp. 45-54. Tagung der Kammer

für Arbeiter und Angestellte für Wien. Vienna: LexisNexis Verlag ARD Orac.

Brandolini, A., and T. M. Smeeding (2005). “Inequality Patterns in Western Democracies:

Cross-Country Differences and Time Changes”, paper presented at the conference

“Democracy, Inequality and Representation: Europe in Comparative Perspective”,

Maxwell School, Syracuse University, Syracuse, 6-7 May 2005.

Brandolini, A., and T. M. Smeeding (2006). “Inequality: International Evidence”.

Forthcoming in S. N. Durlauf and L. E. Blume, The New Palgrave Dictionary of

Economics. Basingstoke: Palgrave Macmillan.

Brandolini, A., L. Cannari, G. D’Alessio and I. Faiella (2006). “Household Wealth

Distribution in Italy in the 1990s”. Forthcoming in E. N. Wolff (ed.), International

Perspectives on Household Wealth. Cheltenham: Edward Elgar. Early available in

Banca d’Italia, Temi di discussione, No. 530, December 2004.

21

Brugiavini, A., K. Maser and A. Sundén (2005). “Measuring Pension Wealth”, paper prepared

for LWS Workshop: “Construction and Usage of Comparable Microdata on Wealth: the

LWS”, Banca d’Italia, Perugia, Italy, 27-29 January 2005.

Butz, W. P., and B. B. Torrey (2006). “Some Frontiers in Social Science”. Science, vol. 312,

pp. 1898-1900.

Campbell, J. Y (2006). “Household Finance”. National Bureau of Economic Research,

Working Paper, No. 12149, March.

Davies, J. B., and A. F. Shorrocks (2000). “The Distribution of Wealth”, in A. B. Atkinson

and F. Bourguignon (eds.), Handbook of Income Distribution. Volume 1, pp. 605-75.

Amsterdam: North-Holland.

Expert Group on Household Income Statistics – The Canberra Group (2001). Final report and

recommendations, Ottawa, The Canberra Group.

Eurostat (2006). Financial accounts. Available at:

http://epp.eurostat.ec.europa.eu/portal/page?_pageid=0,1136173,0_45570701&_dad=po

rtal&_schema=PORTAL&screen=ExpandTree&open=/economy/fina/fina_st&product=

EU_economy_finance&nodeid=36598&vindex=5&level=3&portletid=39994106_QUE

ENPORTLET_92281242&scrollto=0.

Faiella, I., and A. Neri (2004). “La ricchezza delle famiglie italiane e americane”, Banca

d’Italia, Temi di discussione, No. 501, June.

Guiso, L., M. Haliassos and T. Jappelli (2002). “Introduction”. In L. Guiso, M. Haliassos and

T. Jappelli (eds.), Household Portfolios, pp. 1-24 Cambridge, Mass.: MIT Press.

Harding, T., H. O. Aa. Solheim and A. Benedictow (2004). “House ownership and taxes”.

Statistics Norway, Research Department, Discussion Papers No. 395, November.

Kennickell, A. B. (2000). “Wealth Measurement in the Survey of Consumer Finances:

Methodology and Directions for Future Research”. Board of Governors of the Federal

Reserve Board, SCF Working Paper, May.

Kessler, D., and E. N. Wolff (1991). “A Comparative Analysis of Household Wealth Patterns

in France and the Unites States”. Review of Income and Wealth 37, 249-266.

Klevmarken, A. (2006). “The Distribution of Wealth in Sweden: Trends and Driving

Factors”. In G. Chaloupek and T. Zotter (eds.), Steigende wirtschaftliche Ungleichheit

bei steigendem Reichtum?, pp. 29-44. Tagung der Kammer für Arbeiter und Angestellte

für Wien. Vienna: LexisNexis Verlag ARD Orac.

Klevmarken, A., J. Lupton and F. Stafford (2003). “Wealth Dynamics in the 1980s and 1990s.

Sweden and the United States”. Journal of Human Resources 38, pp. 322-353.

22

Martins, N., and E. Villanueva (2006). “Does Limited Access to Mortgage Debt Explain Why

Young Adults Live with Their Parents?”, Unpublished Manuscript, February.

Office for National Statistics (2006). United Kingdom National Accounts. The Blue Book

2006. Edited By Dye, J., and J. Sosimi. Basingstoke: Palgrave Macmillan.

Sierminska, E. (2005). “The Luxembourg Wealth Study: A Progress Report,” paper prepared

for LWS Workshop: “Construction and Usage of Comparable Microdata on Wealth: the

LWS”, Banca d’Italia, Perugia, Italy, 27-29 January 2005.

Smeeding, T. M. (2004). “Twenty Years of Research on Income Inequality, Poverty, and

Redistribution in the Developed World: Introduction and Overview”. Socio-Economic

Review 2, 149-163.

Statistics Canada (2006a). Assets and debts by family units, including employer-sponsored

registered pension plans, by province. Available at:

http://www40.statcan.ca/l01/cst01/famil99k.htm?sdi=assets%20debts.

Statistics Canada (2006b). National balance sheet accounts, market value, by sectors, at

quarter end, quarterly (dollars x 1,000,000). Available at: http://cansim2.statcan.ca/cgi-

win/cnsmcgi.exe?Lang=E&Accessible=1&ArrayId=V1074&ResultTemplate=CII\SNA

___&RootDir=CII/&Interactive=1&OutFmt=HTML2D&Array_Retr=1&Dim=-

#HERE.

Statistics Sweden (2004). Förmögenhetsstatistik 2002.

Table 1. LWS household wealth surveys

Country Name Agency Wealth year (1)

Income year Type of source Over-sam-pling of the wealthy

Sample size No. of non-missing net worth

No. of wealth items

Austria Survey of Household Financial Wealth (SHFW)

Österreichische Nationalbank 2004 2004 Sample survey No 10

Canada Survey of Financial Security (SFS) Statistics Canada 1999 1998 Sample survey Yes 15,933 15,933 17

Cyprus Cyprus Survey of Consumer Finances (SCF)

Central Bank of Cyprus and University of Cyprus

2002 2001 Sample survey Yes 895 349 24

Finland Household Wealth Survey (HWS) Statistics Finland End of 1998 1998 Sample survey No 3,893 3,893 23

Germany Socio-Economic Panel (SOEP) Deutsches Institut Für Wirt-schaftsforschung (DIW) Berlin

2002 2001 Sample panel survey

Yes 12,692 12,129 9

Italy Survey of Household Income and Wealth (SHIW)

Bank of Italy End of 2002 2002 Sample survey (panel section)

No 8,011 8,010 34

Norway Income Distribution Survey (IDS) Statistics Norway End of 2002 2002 Sample survey plus administra-tive records

No 22,870 22,870 35

Sweden Wealth Survey (HINK) Statistics Sweden End of 2002 2002 Sample survey plus administra-tive records

No 17,954 17,954 26

United Kingdom British Household Panel Survey (BHPS)

ESRC 2000 2000 Sample panel survey

No 4,867 (2) 4,185 7

United States Panel Study of Income Dynamics (PSID)

Survey Research Center of the University of Michigan

2001 2000 Sample panel survey

No 7,406 7,071 14

Survey of Consumer Finances (SCF)

Federal Reserve Board and U.S. Department of Treasury

2001 2000 Sample survey Yes 4,442 (3) 4,442 (3) 30

Source: LWS database. (1) Values refer to the time of the interview unless otherwise indicated. (2) Original survey sample. Sample size can rise to 8,761 when weights are not used. (3) Data are stored as five successive replicates of each record that should not be used separately; thus, actual sample size for users is 22,210. The special sample of the wealthy includes 1,532 households.

Table 2. Wealth classification matrix in LWS

Asset or liability LWS acronym

Canada Cyprus Finland Germany Italy Norway Sweden United Kingdom

United States

United States

SFS 1999 SCF 2002 HWS 1998 SOEP 2002 SHIW 2002 IDS 2002 HINK 2002 BHPS 2000 PSID 2001 SCF 2001

FINANCIAL ASSETS Total TFA Σ Σ Σ Σ Σ Σ Σ Σ Σ Σ Deposit accounts: transaction, savings and CDs DA Y Y Y Y Y Y Y (2) Y Total bonds: savings and other bonds TB Y Y Y Y Y Y Y Stocks ST Y Y Y Y Y Y Y Mutual funds and other investment funds TM Y Y Y

Y (1)

Y Y Y Y Y Y

Life insurance LI – Y Y – Y – Y (2) Y Other financial assets (exc. pension) OFA Y Y Y Y Y Y (5) – Y (4) Y Pension assets PA Y Y Y

Y (3) – Y – – Y Y

NON-FINANCIAL ASSETS Total TNF Σ Σ Σ Σ Σ Σ Σ Σ Σ Σ Principal residence PR Y Y Y Y Y Y Y Y Y Y Investment real estate IR Y Y Y Y Y Y Y Y (7) Y Business equity BE Y Y – Y (6) Y Y (6) Y (6) Y (6) Y Y Vehicles VH Y Y Y Y (8) Y Y – Y (9) Y (9) Y Durables and collectibles DRCL – Y Y Y Y – – – Y Other non-financial assets ONF Y – – – – – Y (5) – – Y

LIABILITIES Total TD Σ Σ Σ Σ Σ Y Y Σ Σ Σ Home secured debt HSD Σ Σ Σ – Y (10) Σ Σ Principal residence mortgage MG Y Y Y – Y Y Other property mortgage OMG Y Y Y Y (11) – Y (7) Y Other home secured debt (incl. line of credit) OHSD Y –

Y

–

Y

Y –

Y

– Y Vehicle loans VL Y Y Y Y (9) Y (9) Y Installment debt (incl. credit card balance) IL Y Y

Y Y

Y (11) Y (10) Y

Educational loans EL Y Y Y – Y Y Y Other loans from financial institutions OL Y Y – Y Y Y Informal debt ID

Y Y –

Y

Y – Y

Y (12) Y

Y

Source: LWS database, β-version (July 15, 2006). “Y” denotes a recorded item; “–” denotes a not recorded item; “Σ” indicates that the variable is obtained by aggregation of its components. (1) Excludes checking deposits. (2) DA and LI recorded together. (3) Includes only some pension assets. (4) Includes collectibles and some mutual funds not included in TB. (5) OFA and ONF recorded together. (6) Business assets only. (7) IR recorded net of OMG. (8) As recorded in the 2003 wave. (9) VH recorded net of VL. (10) HSD, VL and IL recorded together. (11) MG, OMG, VL and IL recorded together. (12) Includes also VL, which implies a double-counting.

Table 3. Reconciling the LWS and national net worth concept (averages in thousands of national currencies)

Wealth variable Canada Finland Italy Sweden United States

SFS 1999 HWS 1998 SHIW 2002 HINK 2002 SCF 2001

LWS net worth 102.5 69.3 154.2 537.8 213.1 + pension assets 83.0 0.6 – – 74.4 + other financial assets 2.5 1.6 0.3 24.5 13.1 + business equity 26.9 – 23.5 80.0 (1) 74.7 + other non-financial assets 28.5 6.5 24.4 17.8 20.6

LWS adjusted net worth 243.4 78.0 (2) 202.4 660.1 395.9 LWS coverage ratio (3) 42.1 88.8 76.2 81.5 53.8

National source net worth 249.3 79.8 204.4 660.0 395.5

Source: LWS database, β-version (July 15, 2006) and country sources: Statistics Canada (2006a); Finnish data provided by Markus Säylä; Brandolini et al. (2006); Statistics Sweden (2004); Aizcorbe, Kennickell and Moore (2003). Household weights are used. (1) Business assets only. (2) It does not include other debts. (3) Percentage ratio of LWS net worth to LWS adjusted net worth.

Table 4. Share of missing values in major components of LWS net worth (per cent)

Wealth variable Canada Cyprus Finland Germany Italy Norway Sweden United Kingdom

United States

United States

SFS 1999 SCF 2002 HWS 1998 SOEP 2002 SHIW 2002 IDS 2002 HINK 2002 BHPS 2000 PSID 2001 SCF 2001

Non-financial assets – 25 – 3 0.0001 – – 2 2 – Financial assets – 21 – 4 – – – 9 - – Debt – 43 – 3 – – – 7 3 – Net worth – 61 – 4 0.0001 – – 14 5 –

Sample size 15,933 895 3,893 12,692 8,011 22,870 17,954 4,867 7,406 4,442

Source: LWS database, β-version (July 15, 2006).

Table 5. Per capita household wealth in LWS database and national balance sheets (euros and per cent)

Wealth variable Canada Cyprus Finland Germany Italy Norway Sweden United Kingdom

United States

United States

SFS 1999 SCF 2002 HWS 1998 SOEP 2002 SHIW 2002 IDS 2002 HINK 2002 BHPS 2000 PSID 2001 SCF 2001

LWS database Non-financial assets 28,237 32,763 31,920 53,507 50,965 14,605 33,132 61,436 63,170 77,686 Financial assets 8,018 6,294 6,181 7,971 8,913 22,066 12,943 11,036 31,332 47,059 Debt 9,577 3,719 6,032 11,202 2,590 29,561 16,159 13,572 20,857 26,707 Net worth 26,678 35,339 32,069 50,276 57,288 7,110 29,916 58,901 73,646 98,037

National balance sheet Non-financial assets 32,492 – – 69,234 78,417 – – 67,728 66,679 Financial assets 51,157 38,099 20,317 44,731 48,780 42,268 40,927 87,199 123,768 Debt 13,813 15,825 7,147 18,750 7,089 33,629 16,577 20,471 31,003 Net worth 69,836 – – 95,215 120,108 – – 134,457 159,444

Ratio of LWS to NBS Non-financial assets 87 – – 77 65 – – 91 95 117 Financial assets 16 17 30 18 18 52 32 13 25 38 Debt 69 23 84 60 37 88 97 66 67 86 Net worth 38 – – 53 48 – – 44 46 61

Source: LWS database, β-version (July 15, 2006) and country sources: Eurostat (2006) for financial assets and debt of European countries; personal communication by Ulf von Kalckreuth, Brandolini et al. (2006) and Office for National Statistics (2006) for non-financial wealth in Germany, Italy and the United Kingdom, respectively; Statistics Canada (2006b); Board of Governors of the Federal Reserve System (2006). LWS figures are given by the ratios between wealth totals and number of persons in each survey; household weights are used. National balance sheets (NBS) figures are obtained by dividing total values for the sector “Households and non-profit institutions serving households” by total population. All values are expressed in euros at current prices by using the average market exchange rate in the relevant year.

Table 6. Demographic structure

Household characteristic

Canada Cyprus Finland Germany Italy Norway Sweden United Kingdom

United States

United States

SFS 1999 SCF 2002 HWS 1998 SOEP 2002 SHIW 2002 IDS 2002 HINK 2002 BHPS 2000 PSID 2001 SCF 2001

Mean household size 2.43 3.35 2.16 2.14 2.65 2.14 1.96 2.35 2.38 2.43

Mean age of the household’s head 47 49 49 52 55 49 51 53 48 49

Age composition of household’s head (%)

24 or less 5.9 1.0 7.3 3.7 0.7 7.2 6.6 3.8 5.3 5.6 25-34 19.6 21.3 16.7 15.2 9.4 19.3 16.9 14.3 18.6 17.1 35-44 24.7 24.7 20.0 20.6 21.5 19.4 17.7 19.3 22.2 22.3 45-54 19.6 16.9 21.0 17.5 18.8 18.0 17.5 17.4 22.4 20.6 55-64 11.9 15.4 13.8 16.5 16.9 14.1 16.6 14.9 12.5 13.3 65-74 10.4 15.0 11.7 14.9 18.2 9.8 10.9 14.0 10.9 10.7 75 and over 7.9 5.7 9.5 11.6 14.5 12.2 13.8 16.3 8.1 10.4 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Source: LWS database, β-version (July 15, 2006). Household weights are used.

Table 7. Household asset participation (per cent)

Wealth variable Canada Cyprus Finland Germany (1) Italy Norway Sweden United Kingdom

United States

United States

SFS 1999 SCF 2002 HWS 1998 SOEP 2002 SHIW 2002 IDS 2002 HINK 2002 BHPS 2000 PSID 2001 SCF 2001

All assets as recorded

Non-financial assets 64 76 68 43 72 72 57 70 65 70 Principal residence 60 74 64 39 69 64 53 69 64 68 Investment real estate 16 17 27 13 22 30 14 8 – 17

Financial assets 90 86 92 50 81 99 79 80 83 91 Deposit accounts 88 78 91 – 81 99 59 76 82 91 Bonds 14 44 3 – 14 – 16 – – 19 Stocks 11 40 33 – 10 22 36 – 30 21 Mutual funds 14 1 3 – 13 38 58 – – 18

Debt 68 65 52 30 22 80 70 59 68 75 Home secured debt 41 – 28 – 10 – – 39 – 46

Only financial assets and non-housing debt exceeding 2,500 euros

Non-financial assets 64 – 68 43 72 72 – 70 65 70 Financial assets 48 – 53 49 70 70 – 58 56 60 Total debt 58 – 45 30 17 74 – 49 59 65

Source: LWS database, β-version (July 15, 2006). Household weights are used. (1) Most of financial assets and non-housing debt are recorded only for values exceeding 2,500 euros.

Table 8. Household portfolio composition (percentage share of total assets)

Wealth variable Canada Cyprus (1) Finland Germany (2) Italy Norway (3) Sweden United Kingdom

United States

United States

SFS 1999 SCF 2002 HWS 1998 SOEP 2002 SHIW 2002 IDS 2002 HINK 2002 BHPS 2000 PSID 2001 SCF 2001

Non-financial assets 78 – 84 87 85 – 72 83 67 62 Principal residence 64 – 64 64 68 – 61 74 52 45 Real estates 13 – 20 23 17 – 11 9 14 17

Financial assets 22 – 16 13 15 – 28 17 33 38 Deposit accounts 9 – 10 – 8 – 11 9 10 10 Bonds 1 – 0 – 3 – 2 – – 4 Stocks 7 – 6 – 1 – 6 – 23 15 Mutual funds 5 – 1 – 3 – 9 – – 9

Total assets 100 – 100 100 100 – 100 100 100 100

Debt 26 – 16 18 4 – 35 21 22 21 of which: home secured 22 – 11 – 2 – – 18 – 18

Net worth 74 – 84 82 96 – 65 79 78 79

Source: LWS database, β-version (July 15, 2006). Household weights are used. Shares are computed as ratios of means. Figures may not add up because of rounding. (1) Figures not reported, because over 60 per cent of values for net worth are missing. (2) Most of financial assets and non-housing debt are recorded only for values exceeding 2,500 euros. (3) Figures not reported because valuing real estate on a taxable basis and debt at market prices causes a major inconsistency (indeed, the majority of households have non positive net worth).

Table 9. Distribution of household net worth (per cent)

Statistics Canada Cyprus (1) Finland Germany (2) Italy Norway (3) Sweden United Kingdom

United States

United States

SFS 1999 SCF 2002 HWS 1998 SOEP 2002 SHIW 2002 IDS 2002 HINK 2002 BHPS 2000 PSID 2001 SCF 2001

Positive net worth 77 – 83 63 89 – 68 82 77 77 Nil net worth 3 – 2 29 7 – 5 6 8 4 Negative net worth 20 – 15 9 3 – 27 11 16 19

Quantile/median ratios 10th percentile -17 – -6 0 0 – -84 0 -11 -15 25th percentile 0 – 1 0 8 – -1 2 0 0 75th percentile 350 – 218 886 209 – 447 238 378 368 90th percentile 708 – 390 1,818 359 – 972 482 925 980

Wealth shares Top 10% 53 – 45 54 42 – 58 45 64 71 Top 5% 37 – 31 36 29 – 41 30 49 58 Top 1% 15 – 13 14 11 – 18 10 25 33

Gini index 75 – 68 78 61 – 89 66 81 84

Source: LWS database, β-version (July 15, 2006). Household weights are used. (1) Figures not reported because over 60 per cent of values for net worth are missing. (2) Most of financial assets and non-housing debt are recorded only for values exceeding 2,500 euros. (3) Figures not reported because valuing real estate on a taxable basis and debt at market prices causes a major inconsistency (indeed, the majority of households have non positive net worth).

32

Figure 1. Fraction of holders, by age of the household’s heads (per cent)

Positive Net Worth

0

10

20

30

40

50

60

70

80

90

100

24 andless

25-34 35-44 45-54 55-64 65-75 75 andover

Age

Financial Assets

0

10

20

30

40

50

60

70

80

90

100

24 andless

25-34 35-44 45-54 55-64 65-75 75 andover

Age

Debt

0

10

20

30

40

50

60

70

80

90

100

24 andless

25-34 35-44 45-54 55-64 65-75 75 andover

Age

Principal Residence

0

10

20

30

40

50

60

70

80

90

100

24 andless

25-34 35-44 45-54 55-64 65-75 75 andover

Age

02

1 2 3 4 5 6 7Canada 1999 Cyprus 2002 Finland 1998Germany 2002 (1) Italy 2002 Norway 2002Sweden 2002 United Kingdom 2000 United States 2001

Source: LWS database, β-version (July 15, 2006). Household weights are used. (1) Most of financial assets and non-housing debt are recorded only for values exceeding 2,500 euros.

33

Figure 2. LWS country rankings by mean and median of net worth and income (2002 U.S. dollars)

Mean net worth

0

50,000

100,000

150,000

200,000

250,000Sw

eden

Finl

and

Can

ada

Ger

man

y

UK

US

(PSI

D)

Italy

US

(SC

F)

Median net worth

0

50,000

100,000

150,000

200,000

250,000

Swed

en

Ger

man

y

Can

ada

US

(PSI

D)

US

(SC

F)

Finl

and

UK

Italy

Mean income

0

10,000

20,000

30,000

40,000

Finl

and

Italy

Swed

en

Ger

man

y

Can

ada

UK

US

(SC

F)

US

(PSI

D)

Median income

0

10,000

20,000

30,000

40,000Ita

ly

Finl

and

Swed

en

Ger

man

y

UK

Can

ada

US

(SC

F)

US

(PSI

D)

Source: LWS database, β-version (July 15, 2006). Household weights are used.

34

Figure 3. Median wealth holdings by age of the household’s head (2002 U.S. dollars)

Net worth

0

50,000

100,000

150,000

200,000

under 24 25-34 35-44 45-54 55-64 65-75 75 and over

Financial assets

0

5,000

10,000

15,000

20,000

25,000

under 24 25-34 35-44 45-54 55-64 65-75 75 and over

Debt

0

20,000

40,000

60,000

80,000

under 24 25-34 35-44 45-54 55-64 65-75 75 and over

0100000Canada Cyprus FinlandGermany (1) Italy NorwaySweden United Kingdom United States (SCF)

Source: LWS database, β-version (July 15, 2006). Household weights are used.